China's DeepSeek Large Language Model

Saturday 21 February 2026

China’s DeepSeek large language model arrived as a technical statement and a geopolitical event — a reminder that the race for machine intelligence is not merely about clever algorithms, but about power, trust and the governance of information.

DeepSeek, a Hangzhou-based research lab backed by the hedge fund High-Flyer, became globally prominent with models such as DeepSeek-V3 and DeepSeek-R1, paired with a widely used chatbot application. DeepSeek-R1’s claim to fame is “reasoning” — the attempt to produce step-by-step internal work that improves performance on tasks such as mathematics, programming and structured problem-solving, trained in part through reinforcement learning methods described in the team’s own technical write-up. 

For readers encountering the phrase “large language model” for the first time, it means a statistical system trained on vast quantities of text to predict the next word — and, by doing so, to generate fluent answers, write code, summarise documents and imitate styles of writing. What made DeepSeek unusual was not the existence of yet another model — Silicon Valley and China alike produce them in abundance — but the suggestion that it could reach high performance at relatively low cost, and then distribute model widely enough for others to run it on their own infrastructure.  That combination — capability, affordability and so-called “open-weight” distribution — is precisely what turns a piece of software into a strategic object.

The controversies surrounding DeepSeek follow from that fact.

First, there is the question of data — where it goes, who can access it and what laws apply when it arrives. Reporting on DeepSeek’s consumer application and web interface highlighted that user prompts and related data may be collected and stored in China, and that the system’s behaviour includes visible political filtering on topics sensitive to Beijing.  For private individuals this can be a privacy concern — for companies, journalists and public institutions it becomes a risk-management question, because users inevitably type confidential material into tools that feel conversational and harmless.

Secondly, there is censorship — and, more broadly, narrative alignment. All leading models are shaped by their developers’ preferences and constraints, but DeepSeek has drawn particular attention for avoiding, reframing or refusing certain politically sensitive subjects in ways consistent with the Chinese state’s red lines. This matters for more than culture-war point-scoring. If a model is used at scale — embedded in office software, search engines, tutoring systems and news summarisation tools — then small, systematic omissions accumulate into a quiet form of influence. A model does not need to “persuade” to matter; it merely needs to select what is mentioned, what is not, and what is presented as settled fact.

Thirdly, there is the allegation of intellectual property “free-riding” — in particular, the charge that DeepSeek trained by distilling outputs from United States frontier models. In February 2026, Reuters reported that OpenAI told US lawmakers it had observed accounts associated with DeepSeek employees circumventing access restrictions and programmatically collecting outputs for distillation — a technique that uses a stronger model’s answers to train a new one. If true, this is not simply a commercial dispute. It touches the heart of the current AI business model — that access to a proprietary system, not merely the published papers, is the moat. It also raises a moral argument that will not go away: when the world’s most capable systems are accessible through an interface, how realistic is it to expect that competitors will never treat them as de facto training data?

Fourthly, there is national security — both in the narrow sense (technical compromise) and in the wider sense (dependence). Cybersecurity-oriented commentary has argued that integrating DeepSeek into organisational workflows could expose sensitive information through data collection, poor security practice or hidden interfaces — and that China’s legal environment increases the risk that state bodies can compel access. Even where specific claims are contested, the policy dilemma remains the same: the more powerful and ubiquitous these systems become, the more they resemble critical infrastructure. Democracies have learned — slowly and expensively — that critical infrastructure is a poor place for ambiguity.

Fifthly, there is the matter of export controls and the material reality of computation. DeepSeek’s rise has repeatedly been interpreted through the lens of US restrictions on advanced chips and China’s efforts to work around them. Reuters has described China’s domestic excitement about DeepSeek, and the international anxiety its releases can generate, including the way a new model launch becomes a political signal as much as a product update. In an era where semiconductors are treated as strategic resources, every claim of “low-cost training” or clever efficiency is heard as an argument about whether sanctions work.

Put together, these controversies explain why DeepSeek provokes such heat.

It is not just a chatbot. It is a distribution mechanism for a worldview — and, potentially, for a set of legal and security exposures that travel with it. And you may be using it every day, on different websites or Apps, without knowing it. All the time it may be harvesting your personal information. DeepSeek is also a challenge to the belief, common in the United States until recently, that the artificial intelligence frontier would remain safely behind a wall of capital expenditure and restricted hardware.

For users and institutions deciding what to do, the sensible stance is neither panic nor naïve enthusiasm, but due diligence — technical, legal and editorial.

• Treat prompts as publishable — never place secrets into any external model unless you control the deployment environment. Even if you trust the model you are using, another model may be training from your prompts into the model you trust and harvesting your data.

• Test for political and factual failure in your own domain texts generated by large language models — especially where omission can mislead as effectively as fabrication.

• Separate “open-weight” from “open governance” — a model can be downloadable and still come with constraints, uncertainties about training data and unclear accountability. 

DeepSeek’s deeper lesson is uncomfortable but clarifying. In the twentieth century propaganda travelled through newspapers, radio and television. In the twenty-first it can travel through everyday tools that draft emails, answer homework questions and summarise the news — and it can do so without raising its voice.

That is why the debate about DeepSeek is not merely a debate about China’s engineering prowess. It is in fact a debate about whether democracies are prepared to treat machine intelligence as part of the information environment from which we all draw our news and knowledge — and therefore as something that demands scrutiny, standards and, where necessary, restraint.